Monthly Archives: December 2015

The vast majority of analytics effort is expended on problems that are tactical in nature. That’s not necessarily wrong. Tactics gets a bad rap, sometimes, but the truth is that the vast majority of decisions we make in almost any context are tactical. The problem isn’t that too much analytics is weighted toward tactical issues, it’s really that strategic decisions don’t use analytics at all. The biggest, most important decisions in the digital enterprise nearly always lack a foundation in data or analysis.

I’ve always disliked the idea behind “HIPPOs” – with its Dilbertian assumption that executives are idiots. That isn’t (mostly) my experience at all. But analytics does suffer from what might be described as “virtue” syndrome – the idea that something (say taxes or abstinence) is good for everyone else but not necessarily for me. Just as creative folks tend to think that what they do can’t be driven by analytics, so too is there a perception that strategic decisions must inevitably be more imaginative and intuitive and less number-driven than many decisions further down in the enterprise.

This isn’t completely wrong though it probably short-sells those mid-level decisions. Building good creative takes…creativity. It can’t be churned out by machine. Ditto for strategic decisions. There is NEVER enough information to fully determine a complex strategic decision at the enterprise level.

This doesn’t mean that data isn’t useful or should not be a driver for strategic decisions (and for creative content too). Instinct only works when it’s deeply informed about reality. Nobody has instincts in the abstract. To make a good strategic decision, a decision-maker MUST have certain kinds of data to hand and without that data, there’s nothing on which intuition, knowledge and experience can operate.

You need to know who your audiences are and what makes them distinct. You need (as described in the last post) to understand the different journeys those audiences take and what journeys they like to take. You need to understand why they make the choices they make – what drives them to choose one product or service or another. Things like demand elasticity, brand awareness, and drivers of choice at each journey stage are critical. And, of course, you need to understand when and why those choices might favor the competition.

None of this stuff will make a strategic decision for you. It won’t tell you how much to invest in digital. Whether or not to build a mobile app. Whether personalization will provide high returns.

But without fully understanding audience, journey, drivers of decision and competitive choices, how can ANY digital decision-maker possibly arrive at an informed strategy? They can’t. And, in fact, they don’t. Because for the vast majority of enteprises, none of this information is part-and-parcel of the information environment.

I’ve seen plenty of executive dashboards that are supposed to help people run their business. They don’t have any of this stuff. I’ve seen the “four personas” puffery that’s supposed to help decision-makers understand their audience. I’ve seen how limited is the exposure executives have to journey mapping and how little it is deployed on a day-to-day basis. Worst of all, I’ve seen how absolutely pathetic is the use of voice of customer (online and offline) to help decision-makers understand why customers make the choices they do.

Voice of customer as it exists today is almost exclusively concerned with measuring customer satisfaction. There’s nothing wrong with measuring NPS or satisfaction. But these measures tell you nothing that will help define a strategy. They are at best (and they are often deeply flawed here too) measures of scoreboard – whether or not you are succeeding in a strategy.

I’m sure that people will object that knowing whether or not a strategy is succeeding is important. It is. It’s even a core part of ongoing strategy development. However, when divorced from particular customer journeys, NPS is essentially meaningless and uninterpretable. And while it truly is critical to measure whether or not a strategy is succeeding, it’s even more important to have data to help shape that strategy in the first place.

Executives just don’t get that context from their analytics teams. At best, they get little pieces of it in dribs and drabs. It is never – as it ought to be – the constant ongoing lifeblood of decision-making.

I subtitled this post “The Role of Voice of Customer in Enterprise Analytics” because of all the different types of information that can help make strategic decisions better, VoC is by far the most important. A good VoC program collects information from every channel: online and offline surveys, call-center, site feedback, social media, etc. It provides a continuing, detailed and sliceable view of audience, journey distribution and (partly) success. It’s by far the best way to help decision-makers understand why customers are making the choices they are, whether those choices are evolving, and how those choices are playing out across the competitive set. In short, it answers the majority of the questions that ought to be on the minds of decision-makers crafting a digital strategy.

This is a very different sort of executive dashboard than we typically see. It’s a true customer insights dashboard. It’s also fundamentally different than almost ANY VoC dashboard we see at any level. The vast majority of VoC reporting doesn’t provide slice-and-dice by audience and use-case – a capability which is absolutely essential to useful VoC reporting. VoC reporting is almost never based on and tied into a journey model so that the customer insights data is immediately reflective of journey stage and actionable arena. And VoC reporting almost never includes a continuous focus on exploring customer decision-making and tying that into the performance of actual initiatives.

It isn’t just a matter of a dashboard. One of the most unique and powerful aspects of digital voice-of-customer is the flexibility it provides to rapidly, efficiently and at very little cost tackle new problems. VoC should be a core part of executive decision-making with a constant cadence of research, analysis, discussion and reporting driven by specific business questions. This open and continuing dialog where VoC is a tool for decision-making is critical to integrating analytics into decisioning. If senior folks aren’t asking for new VoC research on a constant basis, you aren’t doing it right. The single best indicator of a robust VoC program in digital is the speed with which it changes.

Sadly, what decision-makers mostly get right now (if they get anything at all) is a high-level, non-segmented view of audience demographics, an occasional glimpse into high-level decision-factors that is totally divorced from both segment and journey stage, and an overweening focus on a scoreboard metric like NPS.

It’s no wonder, given such thin gruel, that decision-makers aren’t using data for strategic decisions better. If our executives mostly aren’t Dilbertian, they aren’t miracle workers either. They can’t make wine out of information water. If we want analytics to support strategy – and I assume we all do – then building a completely different sort of VoC program is the single best place to start. It isn’t everything. There are other types of data (behavioral, benchmark, econometric, etc.) that can be hugely helpful in shaping digital strategies. But a good VoC program is a huge step forward – a step forward that, if well executed – has the power to immediately transform how the digital enterprise thinks and works.

This is probably my last post of the year – so see you in 2016! In the meantime, my book Measuring the Digital World is now available. Could be a great way to spend your holiday down time (ideally while your resting up from time on the slopes)! Have a great holiday…

There is a LOT of cool stuff available in Microsoft’s Project Oxford. If you’re doing text, language or image/video processing there is stuff here worth checking out. Another ton of TNT for the Cognitive explosion!

I got a fair amount of feedback through various channels around my argument that data science isn’t a science and that the scientific method isn’t a method (or at least much of one). I wouldn’t consider either of these claims particularly important in the life of a business analyst, and I think I’ve written pieces that are far more significant in terms of actual practice, but I’ve written few pieces about topics which are evidently more fun to argue about. Well, I’m not opposed to a fun argument now and again, so here’s a redux on some of the commentary and my thoughts in response.

There were two claims in that post:

I was somewhat skeptical that data science was correctly described as a science

I was extremely skeptical that the scientific method was a good description of the scientific endeavor

The comment that most engaged me came from Adam Gitzes and really focused on the first claim:

Science is the distillation of evidence into a causal understanding of the world (my definition anyway). In business analytics, we use surveys, data analysis techniques, and experimental design to also understand causal relationships that can be used to drive our business.

On re-reading my initial post, I realized that while I had argued that business analytics wasn’t science (#1 above), I hadn’t really put many reasons on the table for that view – partly because I was too busy demolishing the “Scientific Method” and partly because I think it’s the less important of the two claims and also the more likely to be correct. Mostly, I just said I was skeptical of the idea. So I think Adam’s right to push out a more specific description of science and ask why data science might not be reasonably described as a kind of scientific endeavor.

I’m not going to get into the thicket of trying to define science. Really. I’m not. That’s the work of a different career. If I got nothing else out of my time studying Philosophy, I got an appreciation for how incredibly hard it is to answer seemingly simple questions like “what is science?” For the most part, we know it when we see it. Physics is science. Philosophy isn’t. But knowing it when you see it is precisely what fails when it comes to edge cases like data science or sociology.

When it comes to business analytics and data science, however, there are a couple of things that make me skeptical of applying the term science that I think we might actually agree on and that use our shared, working understanding of the scientific endeavor.

In business analytics, our main purpose isn’t to understand the world. It’s to improve a specific part of it. Science has no such objective.

Does that seem like a small difference? I don’t think it is. Part of what makes the scientific endeavor unique is that there is no axe to grind. Understanding is the goal. This isn’t to say that people don’t get attached to their ideas or that their careers don’t benefit if they are successful advocates for them – it’s done by humans after all. It would be no more accurate to suggest that the goal of a business is always profit. External forces can and often do set the agenda for researchers. But these are corruptions of the process not the process itself. Business analytics starts (appropriately) with an axe to grind and true science doesn’t.

To see why this makes a difference, consider my own domain – digital analytics. If our goal was just to understand the digital world, we’d have a very different research program than we do. If knowledge was our only goal, we’d spend as much time analyzing why people create certain kinds of digital worlds as how people consume them. That’s not the way it works. In reality, our research program is entirely focused on why and how people use a digital property and what will get more of them to take specific actions – not why and how it was created.

We are, rightly I believe, skeptical of the idea that research sponsored by tobacco companies into lung cancer is, properly speaking, science. That’s not because those researchers don’t follow the general outline of the scientific endeavor – it’s because they have an axe to grind and their research program is determined by factors outside the community of science. When it comes to business analytics, we are all tobacco scientists.

Perhaps we’re not so biased as to the findings of our experiments – good analytics is neutral as to what will work – but we’re every bit as biased when it comes to the outcomes desired and the shape of the research program.

Here’s another crucial difference. I think it’s fair to suggest that in data science we sometimes have no interest in causality. If I’m building a forecast model and I can find variables that are predictive, I may have little interest in whether those variables are also causal. If I’m building a look-alike targeting model, for example, it doesn’t matter one whit whether the variables are causal. Now it’s true that philosophers of science hotly debate the role and necessity of causality in science, but I tend to agree with Adam that there is something in the scientific endeavor that makes the demand for causality a part of the process. But in business analytics, we may demand causality for some problems but be entirely and correctly unconcerned with it in others. In business analytics, causality is a tool not a requirement.

There is, also, the nature of the analytics problem – at least in my field (digital). Science is typically concerned with studying natural phenomena. The digital world is not a natural world, it’s an engineered world. It’s created and adapted with intention. Perhaps even worse, it responds to and changes with the measurements we make and those measurements influence our intentions in subsequent building (which is the whole point after all).

This is Heisenberg’s Uncertainty Principle with a vengeance! When we measure the digital world, we mean to change it based on the measurement. What’s more, once we change it, we can never go back to the same world. We could restore the HTML, but not the absence of users with an alternative experience. In digital, every test we run changes the world in a fundamental way because it changes the users of that world. There is no possibility of conducting a digital test that doesn’t alter the reality we’re measuring – and while this might be true at the quantum level in physics, at the macro level where the scientific endeavor really lives, it seems like a huge difference.

What’s more, each digital property lives in the context of a larger digital world that is being constantly changed with intention by a host of other people. When new Apps like Uber change our expectations of how things like payment should work or alter the design paradigm on the Web, these exogenous and intentional changes can have a dramatic impact on our internal measurement. There is, then, little or no possibility of a true controlled experiment in digital. In digital analytics, our goal is to optimize one part of a giant machine for a specific purpose while millions of other people are optimizing other, inter-related parts of the same machine for entirely different and often opposed purposes.

This doesn’t seem like science to me.

There are disciplines that seem clearly scientific that cannot do controlled experiments. However, no field where the results of an experiment change the measured reality in a clearly significant fashion and are used to intentionally shape the resulting reality is currently described as scientific.

So why don’t I think data science is a science – at least in the realm of digital analytics? It differs from the scientific endeavor in several aspects that seem to me to be critical. Unlike science, business analytics and data science start with an agenda that isn’t just understanding and this fundamentally shapes the research program. Unlike science, business analytics and data science have no fixed commitment to causal explanations – just a commitment to working explanations. Finally, unlike science, business analytics and data science change the world they measure in a clearly significant fashion and do so intentionally with respect to the measurement.

Given that we have no fixed and entirely adequate definition of science, none of this is proof. I can’t demonstrate to you with the certainty of a logical proof that the definition of science requires X, data science is not X, so data science is not a science.

However, I think I have shown that at least by many of the core principles we associate with the scientific endeavor, that business analytics (which I take to be a proxy in this conversation for data science) is not well described as a science.

This isn’t a huge deal. I’ve done business analytics for many years and never once thought of myself as a scientist. What’s more, once we realize that being scientists doesn’t attach a powerful new methodology to business analytics – which was the rather more important point of my last post – it’s much less clear why anyone would think it makes a difference.

Agree?

A few other notes on the comments I received. With regards to Nikolaos’ question “why should we care?” I’m obviously largely in agreement. There is intellectual interest in these questions (at least for me), but I won’t pretend that they are likely to matter in actual practice or will determine ‘what works’. I’m also very much in agreement with Ake’s point about qualitative data. The truth is that nothing in the scientific endeavor precludes the use of qualitative data in addition to behavioral data. But even though there’s no determinate tie between the two, I certainly think that advocates for data science as a science are particularly likely to shun qualitative data (which is a shame). As far as Patrick’s comment goes, I think it dodges the essential question. He’s right to suggest that the term data science is contentless because data is not the subject of science, the data is always about something which is the subject of science. But I take the deeper claim to be what I have tackled here; namely, that business analytics is a scientific endeavor. That claim isn’t contentless, just wrong. I remain, still, deeply unconvinced of the utility of CRISP-DM.

I came across an interesting read recently on the definition of both data scientist and data science. Now, even though I’m about to disagree with almost everything in the article, that doesn’t mean I think it’s wrong-headed or not worth a read. It’s a fairly conventional, industry standard view of the world and provides a common-sense and reasonable set of definitions for both data scientist and data science. I’d encourage you take a look if you’re interested in this type of question.

Meanwhile, if you’re willing to rely on my summary, here’s what I take to be the gist of the article:

Data Science is about finding insights in data to make better decisions

Using survey techniques and asking data professionals to classify their skills, there are four major styles of data scientist. Three styles (business management professionals, developers, and researchers) map directly to the three key skills elaborated above (subject matter expertise, programming and statistics). Then there’s a fourth category appropriately titled “Creatives” who aren’t good at any of these skills…okay I jest…perhaps it’s more fair to say they are balanced fairly equally across the skill sets.

Popular analytics methods (SMART and CRISP-DM) are essentially no more than variants of the “Scientific Method” and, when you get right down to it, data science is nothing more (or less since the diminutive is not meant to imply anything) than the application of that method to whatever problem a data professional is trying to solve. In other words, and here I quote directly, “data science just is science”.

Science works via the “Scientific Method” described as:

Formulate a question or problem statement

Generate a hypothesis that is testable

Gather/Generate data

Analyze data to test the hypotheses / Draw conclusions

Communicate results to interested parties or take action

That’s it. And you’re probably wondering how or why I would disagree with any of this since it’s pretty innocuous stuff. Yes, I’ve written in the past about my suspicions around the whole ‘data science’ term – though heaven knows I use it myself since the market seems to reward it. Taken as it generally is, it’s either a cunning replacement for the label statistician (since we all “know” statisticians aren’t much use when it comes to driving business value) or a demand that analysts should have “full-stack” skills. I don’t necessarily buy the idea that full-stack skills are critical or that there’s a huge benefit in combining them in a single person instead of spreading them across a team, but it’s not something I lose sleep over.

What’s more, once you start flavoring data scientists based on their real proficiencies inside that three-part set, you’re really just back to having analysts (the subject matter expertise folks), programmers, and statisticians. The same people you always had except now they call themselves data scientists and charge you quite a bit more for doing the same stuff they’ve always done. Since I’m one of those people, I not deeply opposed to the whole trend. Here’s a way to think about all this that I think is a little more useful.

None of which is really worth bothering to disagree about though. It’s semantics of a fairly uninteresting sort.

No, what really bothers me about this conventional view is encapsulated in the last two claims: #4 and #5. The idea that data science is science and that the scientific method is applicable to business analytics. I’m not at all sure that business analytics is or should aspire to be science and I’m quite sure that the scientific method won’t save us.

On the other hand, I agree with the first part of the claim in #4. Namely, that methodologies like CRISP-DM are just faintly warmed over versions of the scientific method.

Despite what most people would assume, that’s not a good thing and here I’m going to go all “philosophy guy” on you to explain why, and also why I think this is actually a pretty important point.

Debunking the Scientific Method

In the past five hundred years, the dominant theme in Western culture has been the continuing and astonishing success of the scientific endeavor. Only the most hardened skeptic could doubt the importance and success of scientific disciplines like physics, chemistry and biology in dramatically improving our understanding of the natural world. When it comes to the success of the scientific endeavor, I’m not skeptical at all. It’s worked and it’s worked amazingly well.

But why is that?

The popular conception is that science works because scientists apply the scientific method – testing theories experimentally and proving or refuting them. It’s the five step process enumerated above.

And it just isn’t right. Since way back in the day when I was studying philosophy of science, there’s been a broad consensus that the “scientific method” is a deeply flawed account of the scientific endeavor. Karl Popper provided the best and most influential account of the traditional scientific method and the importance of refutation as opposed to proof. Thomas Kuhn pretty much debunked that explanation as an historical account of how science actually works (despite having his own deeply unsuccessful explanation) and Quine absolutely destroyed it as an intellectual model. It turns out that it’s basically impossible to refute a single hypothesis in isolation with an experiment. Quine actually influenced my thinking on why KPIs, taken in isolation, are always useless. Depending on the background assumptions, any change of a KPI (and in any direction) can have diametrically opposed meanings. It’s pretty much the same thing with a hypothesis. You can rescue any hypothesis from experimental refutation by changing the background assumptions. What’s more, Kuhn showed that this happens all the time in science – punctuated by dramatic cases where it doesn’t.

I doubt there is a single working historian or philosopher of science who would accept the “scientific method” as a reasonable explanation for how science works from either an historical or intellectual perspective.

What’s more, the scientific method as popularly elaborated is almost contentless. Strip away the fancy language and it translates into something like this:

Decide what problem you want to solve

Think about the problem until you have an idea of how it might be solved

Try it out and see if it works

Repeat until you solve the problem

Does this feel action guiding and powerful?

It feels to me like the sort of thing you might sell on late-night TV. Available now, limited time only – a one stop absolutely foolproof method for solving any problem of any sort in any field! The Scientific Method! Buy!

The only part of the scientific method that feels significant in any respect is that requirement that your idea should be capable of specific refutation (testable) via experiment. Sadly, that’s exactly the concept that Quine showed to be impossible. So the scientific method as popularly understood is pretty much a bunch of boilerplate with one mistaken idea bolted on.

The idea that this type of general problem solving procedure is the explanation for the success of science seems implausible on its face and is contradicted by experience.

Implausible because the method as described is so contentless. How do I pick which problems to tackle from the infinite set available? The method is silent. How do I generate hypothesis? The method is silent. How do I know they are testable? The method is silent. How do I test them? The method is silent. How do I know what to do when a test doesn’t refute a hypothesis? The method is silent. How many failures to refute a hypothesis is enough to prove it? The method is silent. How do I communicate the results? The method is silent.

If what we want in a methodology is a massively generalized process that provides zero guidance on how to accomplish the tasks it lays out and has one impossible to meet demand, then the scientific method is great.

Hence the implausibility of the claim that the scientific method is a reasonable explanation for why science works. The scientific endeavor is neither defined, nor described, by the scientific method.

On a less important note, I’m not at all sure that it’s correct to think of data science as even potentially a scientific endeavor – at least when it comes to business analytics. The belief that the scientific endeavor works in general is broadly contradicted by experience – it doesn’t work for everything. Yes, the scientific endeavor has worked extraordinarily well in physics and biology. But smart people have tried to emulate the scientific approach in lots of other places too. Fields like history, sociology, philosophy and psychology (and lots of other disciplines as well) have all drunk the “scientific method” moonshine with a conspicuous absence of success. Clearly something about the scientific endeavor makes it very effective for some types of problems and not effective at all for others. That seems to me a pretty important fact to keep in mind when we claim that business analytics and data science are “just science”. It’s comforting to think we can re-cast business as science, but it’s not clear why we should think that’s true. I’ve never thought of business analytics as a truly scientific enterprise and renaming it data science doesn’t make it seem any more likely to be so.

Why CRISP-DM and most other generalized analytics models are the scientific method…and LESS

Unfortunately, methods specific to analytics like CRISP-DM are worse not better. They lack even the idea of specific testability which, though incorrect, at least made some sense as a driver of a method. CRISP-DM lays out a process for analytics that essentially says it works like this: figure out what your problem is, figure out what data you need, setup your data, build your model, check your model, deploy your model.

Wow. That’s very helpful.

Here’s a CRISP-DM like method for becoming President of the United States.

Decide which political party to join

Register as a candidate for president

Create lots of positive press about yourself and your positions

Raise a lot of money

Convince people to vote for you

Armed with a cutting-edge method like this, your path to power is assured. Donald Trump beware!

Really, how different is CRISP-DM from this? It adds a few little flourishes and some academic language but it lives at the same level of empty generality. I suppose it’s good to know that you deploy models only after you build them, but I’m thinking a formal methodology should give us a little more utility than that.

Methodologies like Six Sigma or SPEED (which I laid out last week and which is why this topic is much on my mind and seems important) provide something real and essential – they provide enough guidance to actually drive a process.

As a side note, I’d point out that successful methodologies are nearly always domain specific (SPEED is entirely specific to digital analytics and Six Sigma has been mostly successful in a very specific range of manufacturing production problems) for the simple reason that generality destroys utility when it comes to method.

So is Business Analytics a “Science”?

It’s a real question, then, whether business analytics can reasonably be considered a science and, in fact, it’s a much more ambitious claim than most people would realize (at least when it’s cloaked in the idea that data science is a science – after all, it says science right there in the title). I’m highly skeptical of the idea that data science is science because I’m highly skeptical that business analytics problems are scientific problems.

They don’t seem like it to me. Business analytics problems map very poorly indeed to the natural sciences and only very partially to the social sciences where the track record of the scientific endeavor is, to say the least, mixed.

So claiming that data science is about using the scientific method on data problems might seem like a “Mom and Apple Pie” kind of thing, but I think it’s wrong on two counts.

It’s wrong because business analytics problems are not obviously the types of problems that are scientific. I can’t say for sure that they aren’t – and I might be persuaded otherwise – but first glance I think there are strong reasons for skepticism and little reason to think that advocates of this view really understand what they are saying or have good reasons to back their claim.

It’s especially wrong because the scientific method as popularly understood is neither meaningful nor a method. This is important. In fact, this is the one really important thing you really should take away from this post. If you think hiring data scientists ensures you have a method (and not just a method but a “scientific” one), you’re going to be sadly disappointed. Data scientists don’t arrive at your doorstep complete with a real method for continuous improvement in digital. It doesn’t matter how data sciencey they are. And if you believe that telling your analysts to use the “scientific method” is going to make your analytics more successful…well that, my friend, is even more absurd.

I have strong reasons for thinking that Six Sigma (for example) isn’t an appropriate methodology for digital analytics. But at least it’s a real method. Flawed as it is when applied to digital analytics, it’s rather more likely to drive results than the “scientific” method. And, of course, I have my own axe to grind. The methodology I described in SPEED is purpose-built for digital and is action-guiding. I’d love to have people adopt and use it. But even if you don’t like SPEED, the importance of having a real method and using that method to drive continuous improvement shouldn’t be discounted.

Go ahead, build your own. Just make sure it’s not of the “figure out your problem, then solve your problem, then iterate” variety; unless, of course, you want an analytics method to sell on late-night TV.

I promise there’s no (well…very little) philosophy in ‘Measuring the Digital World’ – but I do think there is some good method! It’s available for pre-order now on Amazon.

People have struggled with this (big) data provider model but Factual feels like it’s found a real (and valuable) niche. Would love to see more of this grow since external data is a huge miss in most big data systems.

Targeted VoC is a powerful (and totally neglected) tool for personalization. Facebook’s experience is entirely relevant to ANY content producer. I don’t know if I can take credit for this, but I suggested this to folks at Facebook a couple of years back!

An interesting discussion of the problems in identifying “likely” voters and the benefits of behavioral data integration. Food for thought in the enterprise world as well where the equivalent is often possible but rarely done.